Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations2204
Missing cells2472
Missing cells (%)5.6%
Duplicate rows11
Duplicate rows (%)0.5%
Total size in memory292.8 KiB
Average record size in memory136.1 B

Variable types

DateTime1
Categorical4
Numeric10
Text5

Alerts

indicativo has constant value "6083X" Constant
nombre has constant value "MARBELLA" Constant
provincia has constant value "MALAGA" Constant
altitud has constant value "2.0" Constant
Dataset has 11 (0.5%) duplicate rowsDuplicates
hrMax is highly overall correlated with hrMedia and 1 other fieldsHigh correlation
hrMedia is highly overall correlated with hrMax and 1 other fieldsHigh correlation
hrMin is highly overall correlated with hrMax and 1 other fieldsHigh correlation
racha is highly overall correlated with velmediaHigh correlation
tmax is highly overall correlated with tmed and 1 other fieldsHigh correlation
tmed is highly overall correlated with tmax and 1 other fieldsHigh correlation
tmin is highly overall correlated with tmax and 1 other fieldsHigh correlation
velmedia is highly overall correlated with rachaHigh correlation
tmed has 200 (9.1%) missing values Missing
prec has 57 (2.6%) missing values Missing
tmin has 200 (9.1%) missing values Missing
horatmin has 200 (9.1%) missing values Missing
tmax has 200 (9.1%) missing values Missing
horatmax has 200 (9.1%) missing values Missing
hrMedia has 251 (11.4%) missing values Missing
hrMax has 258 (11.7%) missing values Missing
horaHrMax has 258 (11.7%) missing values Missing
hrMin has 258 (11.7%) missing values Missing
horaHrMin has 258 (11.7%) missing values Missing
prec has 1898 (86.1%) zeros Zeros

Reproduction

Analysis started2025-02-19 18:37:13.782181
Analysis finished2025-02-19 18:37:22.636905
Duration8.85 seconds
Software versionydata-profiling vv4.12.2
Download configurationconfig.json

Variables

fecha
Date

Distinct2193
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size17.3 KiB
Minimum2019-01-01 00:00:00
Maximum2025-01-01 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-02-19T18:37:22.711159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:22.809834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

indicativo
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing13
Missing (%)0.6%
Memory size17.3 KiB
6083X
2191 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters10955
Distinct characters5
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6083X
2nd row6083X
3rd row6083X
4th row6083X
5th row6083X

Common Values

ValueCountFrequency (%)
6083X 2191
99.4%
(Missing) 13
 
0.6%

Length

2025-02-19T18:37:22.893190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-19T18:37:22.932358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
6083x 2191
100.0%

Most occurring characters

ValueCountFrequency (%)
6 2191
20.0%
0 2191
20.0%
8 2191
20.0%
3 2191
20.0%
X 2191
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8764
80.0%
Uppercase Letter 2191
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 2191
25.0%
0 2191
25.0%
8 2191
25.0%
3 2191
25.0%
Uppercase Letter
ValueCountFrequency (%)
X 2191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 8764
80.0%
Latin 2191
 
20.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 2191
25.0%
0 2191
25.0%
8 2191
25.0%
3 2191
25.0%
Latin
ValueCountFrequency (%)
X 2191
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10955
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 2191
20.0%
0 2191
20.0%
8 2191
20.0%
3 2191
20.0%
X 2191
20.0%

nombre
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing13
Missing (%)0.6%
Memory size17.3 KiB
MARBELLA
2191 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters17528
Distinct characters6
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMARBELLA
2nd rowMARBELLA
3rd rowMARBELLA
4th rowMARBELLA
5th rowMARBELLA

Common Values

ValueCountFrequency (%)
MARBELLA 2191
99.4%
(Missing) 13
 
0.6%

Length

2025-02-19T18:37:22.978467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-19T18:37:23.016725image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
marbella 2191
100.0%

Most occurring characters

ValueCountFrequency (%)
A 4382
25.0%
L 4382
25.0%
M 2191
12.5%
R 2191
12.5%
B 2191
12.5%
E 2191
12.5%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 17528
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 4382
25.0%
L 4382
25.0%
M 2191
12.5%
R 2191
12.5%
B 2191
12.5%
E 2191
12.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 17528
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 4382
25.0%
L 4382
25.0%
M 2191
12.5%
R 2191
12.5%
B 2191
12.5%
E 2191
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17528
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 4382
25.0%
L 4382
25.0%
M 2191
12.5%
R 2191
12.5%
B 2191
12.5%
E 2191
12.5%

provincia
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing13
Missing (%)0.6%
Memory size17.3 KiB
MALAGA
2191 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters13146
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMALAGA
2nd rowMALAGA
3rd rowMALAGA
4th rowMALAGA
5th rowMALAGA

Common Values

ValueCountFrequency (%)
MALAGA 2191
99.4%
(Missing) 13
 
0.6%

Length

2025-02-19T18:37:23.062674image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-19T18:37:23.099168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
malaga 2191
100.0%

Most occurring characters

ValueCountFrequency (%)
A 6573
50.0%
M 2191
 
16.7%
L 2191
 
16.7%
G 2191
 
16.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 13146
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 6573
50.0%
M 2191
 
16.7%
L 2191
 
16.7%
G 2191
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Latin 13146
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 6573
50.0%
M 2191
 
16.7%
L 2191
 
16.7%
G 2191
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13146
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 6573
50.0%
M 2191
 
16.7%
L 2191
 
16.7%
G 2191
 
16.7%

altitud
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing13
Missing (%)0.6%
Memory size17.3 KiB
2.0
2191 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters6573
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row2.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 2191
99.4%
(Missing) 13
 
0.6%

Length

2025-02-19T18:37:23.144877image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-02-19T18:37:23.182670image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2.0 2191
100.0%

Most occurring characters

ValueCountFrequency (%)
2 2191
33.3%
. 2191
33.3%
0 2191
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4382
66.7%
Other Punctuation 2191
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2191
50.0%
0 2191
50.0%
Other Punctuation
ValueCountFrequency (%)
. 2191
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6573
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2191
33.3%
. 2191
33.3%
0 2191
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6573
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2191
33.3%
. 2191
33.3%
0 2191
33.3%

tmed
Real number (ℝ)

High correlation  Missing 

Distinct190
Distinct (%)9.5%
Missing200
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean19.476747
Minimum9.3000002
Maximum31.200001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-02-19T18:37:23.243283image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum9.3000002
5-th percentile13.4
Q116
median18.799999
Q322.725
95-th percentile27
Maximum31.200001
Range21.900002
Interquartile range (IQR)6.7250004

Descriptive statistics

Standard deviation4.2533836
Coefficient of variation (CV)0.21838266
Kurtosis-0.81078994
Mean19.476747
Median Absolute Deviation (MAD)3.3000002
Skewness0.31905672
Sum39031.4
Variance18.091272
MonotonicityNot monotonic
2025-02-19T18:37:23.332766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.79999924 37
 
1.7%
16 35
 
1.6%
15.19999981 34
 
1.5%
17.79999924 33
 
1.5%
15.80000019 32
 
1.5%
18 31
 
1.4%
16.60000038 31
 
1.4%
15 30
 
1.4%
16.20000076 30
 
1.4%
14.60000038 29
 
1.3%
Other values (180) 1682
76.3%
(Missing) 200
 
9.1%
ValueCountFrequency (%)
9.300000191 1
< 0.1%
9.800000191 1
< 0.1%
10 1
< 0.1%
10.5 1
< 0.1%
10.60000038 2
0.1%
10.80000019 1
< 0.1%
10.89999962 1
< 0.1%
11 1
< 0.1%
11.19999981 2
0.1%
11.5 1
< 0.1%
ValueCountFrequency (%)
31.20000076 1
 
< 0.1%
29.79999924 2
 
0.1%
29.60000038 1
 
< 0.1%
29.5 1
 
< 0.1%
29.39999962 3
0.1%
29.20000076 3
0.1%
29.10000038 1
 
< 0.1%
29 5
0.2%
28.79999924 2
 
0.1%
28.70000076 2
 
0.1%

prec
Real number (ℝ)

Missing  Zeros 

Distinct96
Distinct (%)4.5%
Missing57
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean0.88299953
Minimum0
Maximum108.8
Zeros1898
Zeros (%)86.1%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-02-19T18:37:23.424892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3.8
Maximum108.8
Range108.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.9879055
Coefficient of variation (CV)5.6488201
Kurtosis185.8472
Mean0.88299953
Median Absolute Deviation (MAD)0
Skewness11.456765
Sum1895.8
Variance24.879202
MonotonicityNot monotonic
2025-02-19T18:37:23.516275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1898
86.1%
0.200000003 32
 
1.5%
0.400000006 22
 
1.0%
0.8000000119 13
 
0.6%
0.6000000238 7
 
0.3%
1.399999976 7
 
0.3%
1.200000048 7
 
0.3%
2.599999905 6
 
0.3%
2.799999952 6
 
0.3%
3.799999952 5
 
0.2%
Other values (86) 144
 
6.5%
(Missing) 57
 
2.6%
ValueCountFrequency (%)
0 1898
86.1%
0.200000003 32
 
1.5%
0.400000006 22
 
1.0%
0.6000000238 7
 
0.3%
0.8000000119 13
 
0.6%
1 5
 
0.2%
1.200000048 7
 
0.3%
1.399999976 7
 
0.3%
1.600000024 5
 
0.2%
1.799999952 5
 
0.2%
ValueCountFrequency (%)
108.8000031 1
< 0.1%
94.40000153 1
< 0.1%
64 1
< 0.1%
43.20000076 1
< 0.1%
42.79999924 1
< 0.1%
38.59999847 1
< 0.1%
37.59999847 1
< 0.1%
34.59999847 1
< 0.1%
33.40000153 1
< 0.1%
32.59999847 1
< 0.1%

tmin
Real number (ℝ)

High correlation  Missing 

Distinct204
Distinct (%)10.2%
Missing200
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean16.797904
Minimum6
Maximum27.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-02-19T18:37:23.604142image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile10.4
Q113.3
median16.200001
Q320.200001
95-th percentile24.200001
Maximum27.9
Range21.9
Interquartile range (IQR)6.9000006

Descriptive statistics

Standard deviation4.344336
Coefficient of variation (CV)0.25862369
Kurtosis-0.7265203
Mean16.797904
Median Absolute Deviation (MAD)3.3000011
Skewness0.24391022
Sum33663
Variance18.873257
MonotonicityNot monotonic
2025-02-19T18:37:23.694974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.30000019 29
 
1.3%
14.19999981 27
 
1.2%
13.89999962 26
 
1.2%
12.10000038 24
 
1.1%
12.80000019 23
 
1.0%
13.39999962 22
 
1.0%
12.89999962 21
 
1.0%
14.60000038 21
 
1.0%
16.20000076 20
 
0.9%
12.39999962 20
 
0.9%
Other values (194) 1771
80.4%
(Missing) 200
 
9.1%
ValueCountFrequency (%)
6 1
< 0.1%
6.5 1
< 0.1%
7 1
< 0.1%
7.099999905 2
0.1%
7.300000191 1
< 0.1%
7.400000095 1
< 0.1%
7.599999905 1
< 0.1%
7.699999809 2
0.1%
7.800000191 1
< 0.1%
7.900000095 1
< 0.1%
ValueCountFrequency (%)
27.89999962 1
 
< 0.1%
27.60000038 2
 
0.1%
27.5 2
 
0.1%
27.29999924 2
 
0.1%
27.10000038 1
 
< 0.1%
27 2
 
0.1%
26.89999962 1
 
< 0.1%
26.79999924 5
0.2%
26.70000076 4
0.2%
26.60000038 2
 
0.1%

horatmin
Text

Missing 

Distinct546
Distinct (%)27.2%
Missing200
Missing (%)9.1%
Memory size17.3 KiB
2025-02-19T18:37:23.896881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0409182
Min length5

Characters and Unicode

Total characters10102
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique214 ?
Unique (%)10.7%

Sample

1st row07:29
2nd row05:41
3rd row06:06
4th rowVarias
5th row06:23
ValueCountFrequency (%)
04:11 83
 
4.1%
varias 82
 
4.1%
04:15 44
 
2.2%
00:11 43
 
2.1%
04:24 43
 
2.1%
04:14 39
 
1.9%
05:15 37
 
1.8%
04:12 36
 
1.8%
04:25 30
 
1.5%
04:16 26
 
1.3%
Other values (536) 1541
76.9%
2025-02-19T18:37:24.157769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2149
21.3%
: 1922
19.0%
1 1142
11.3%
4 943
9.3%
2 912
9.0%
5 829
 
8.2%
3 675
 
6.7%
6 470
 
4.7%
7 261
 
2.6%
8 183
 
1.8%
Other values (6) 616
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7688
76.1%
Other Punctuation 1922
 
19.0%
Lowercase Letter 410
 
4.1%
Uppercase Letter 82
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2149
28.0%
1 1142
14.9%
4 943
12.3%
2 912
11.9%
5 829
 
10.8%
3 675
 
8.8%
6 470
 
6.1%
7 261
 
3.4%
8 183
 
2.4%
9 124
 
1.6%
Lowercase Letter
ValueCountFrequency (%)
a 164
40.0%
i 82
20.0%
r 82
20.0%
s 82
20.0%
Other Punctuation
ValueCountFrequency (%)
: 1922
100.0%
Uppercase Letter
ValueCountFrequency (%)
V 82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9610
95.1%
Latin 492
 
4.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2149
22.4%
: 1922
20.0%
1 1142
11.9%
4 943
9.8%
2 912
9.5%
5 829
 
8.6%
3 675
 
7.0%
6 470
 
4.9%
7 261
 
2.7%
8 183
 
1.9%
Latin
ValueCountFrequency (%)
a 164
33.3%
i 82
16.7%
r 82
16.7%
V 82
16.7%
s 82
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10102
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2149
21.3%
: 1922
19.0%
1 1142
11.3%
4 943
9.3%
2 912
9.0%
5 829
 
8.2%
3 675
 
6.7%
6 470
 
4.7%
7 261
 
2.6%
8 183
 
1.8%
Other values (6) 616
 
6.1%

tmax
Real number (ℝ)

High correlation  Missing 

Distinct189
Distinct (%)9.4%
Missing200
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean22.156687
Minimum11.5
Maximum35.599998
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-02-19T18:37:24.245720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum11.5
5-th percentile16
Q118.6
median21.6
Q325.5
95-th percentile29.9
Maximum35.599998
Range24.099998
Interquartile range (IQR)6.8999996

Descriptive statistics

Standard deviation4.3295317
Coefficient of variation (CV)0.19540519
Kurtosis-0.79425353
Mean22.156687
Median Absolute Deviation (MAD)3.3999996
Skewness0.31812066
Sum44402
Variance18.744844
MonotonicityNot monotonic
2025-02-19T18:37:24.336610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18.20000076 26
 
1.2%
18.89999962 26
 
1.2%
18.39999962 26
 
1.2%
17.5 25
 
1.1%
20.89999962 24
 
1.1%
17.89999962 23
 
1.0%
18.10000038 22
 
1.0%
19.79999924 22
 
1.0%
17.39999962 22
 
1.0%
18 21
 
1.0%
Other values (179) 1767
80.2%
(Missing) 200
 
9.1%
ValueCountFrequency (%)
11.5 1
 
< 0.1%
11.69999981 1
 
< 0.1%
12.89999962 1
 
< 0.1%
13.30000019 1
 
< 0.1%
13.5 1
 
< 0.1%
13.60000038 1
 
< 0.1%
13.80000019 1
 
< 0.1%
14 2
0.1%
14.10000038 3
0.1%
14.30000019 1
 
< 0.1%
ValueCountFrequency (%)
35.59999847 1
 
< 0.1%
35 1
 
< 0.1%
34.09999847 1
 
< 0.1%
32.29999924 1
 
< 0.1%
32.20000076 1
 
< 0.1%
32 1
 
< 0.1%
31.70000076 3
 
0.1%
31.60000038 2
 
0.1%
31.5 4
0.2%
31.29999924 8
0.4%

horatmax
Text

Missing 

Distinct687
Distinct (%)34.3%
Missing200
Missing (%)9.1%
Memory size17.3 KiB
2025-02-19T18:37:24.543493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0199601
Min length5

Characters and Unicode

Total characters10060
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique218 ?
Unique (%)10.9%

Sample

1st row11:10
2nd row15:18
3rd row00:02
4th row12:28
5th row09:33
ValueCountFrequency (%)
varias 40
 
2.0%
16:18 16
 
0.8%
16:28 14
 
0.7%
15:51 12
 
0.6%
15:32 11
 
0.5%
15:13 10
 
0.5%
15:20 10
 
0.5%
00:01 10
 
0.5%
15:34 10
 
0.5%
15:53 10
 
0.5%
Other values (677) 1861
92.9%
2025-02-19T18:37:24.817569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2239
22.3%
: 1964
19.5%
0 1085
10.8%
5 861
 
8.6%
2 716
 
7.1%
4 704
 
7.0%
3 640
 
6.4%
6 491
 
4.9%
7 400
 
4.0%
8 383
 
3.8%
Other values (6) 577
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7856
78.1%
Other Punctuation 1964
 
19.5%
Lowercase Letter 200
 
2.0%
Uppercase Letter 40
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2239
28.5%
0 1085
13.8%
5 861
 
11.0%
2 716
 
9.1%
4 704
 
9.0%
3 640
 
8.1%
6 491
 
6.2%
7 400
 
5.1%
8 383
 
4.9%
9 337
 
4.3%
Lowercase Letter
ValueCountFrequency (%)
a 80
40.0%
s 40
20.0%
r 40
20.0%
i 40
20.0%
Other Punctuation
ValueCountFrequency (%)
: 1964
100.0%
Uppercase Letter
ValueCountFrequency (%)
V 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9820
97.6%
Latin 240
 
2.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2239
22.8%
: 1964
20.0%
0 1085
11.0%
5 861
 
8.8%
2 716
 
7.3%
4 704
 
7.2%
3 640
 
6.5%
6 491
 
5.0%
7 400
 
4.1%
8 383
 
3.9%
Latin
ValueCountFrequency (%)
a 80
33.3%
s 40
16.7%
V 40
16.7%
r 40
16.7%
i 40
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10060
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2239
22.3%
: 1964
19.5%
0 1085
10.8%
5 861
 
8.6%
2 716
 
7.1%
4 704
 
7.0%
3 640
 
6.4%
6 491
 
4.9%
7 400
 
4.0%
8 383
 
3.8%
Other values (6) 577
 
5.7%

dir
Real number (ℝ)

Distinct36
Distinct (%)1.6%
Missing22
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean21.563245
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-02-19T18:37:24.902269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median22
Q326
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)19

Descriptive statistics

Standard deviation21.842238
Coefficient of variation (CV)1.0129384
Kurtosis6.5567928
Mean21.563245
Median Absolute Deviation (MAD)13
Skewness2.5232381
Sum47051
Variance477.08334
MonotonicityNot monotonic
2025-02-19T18:37:24.986485image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
8 306
13.9%
24 236
10.7%
26 214
 
9.7%
7 198
 
9.0%
6 169
 
7.7%
99 130
 
5.9%
25 104
 
4.7%
23 89
 
4.0%
27 81
 
3.7%
22 70
 
3.2%
Other values (26) 585
26.5%
ValueCountFrequency (%)
1 21
 
1.0%
2 38
 
1.7%
3 23
 
1.0%
4 66
 
3.0%
5 53
 
2.4%
6 169
7.7%
7 198
9.0%
8 306
13.9%
9 67
 
3.0%
10 50
 
2.3%
ValueCountFrequency (%)
99 130
5.9%
36 49
 
2.2%
35 25
 
1.1%
34 26
 
1.2%
33 13
 
0.6%
32 18
 
0.8%
31 13
 
0.6%
30 19
 
0.9%
29 12
 
0.5%
28 44
 
2.0%

velmedia
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)2.4%
Missing14
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean3.9757991
Minimum0.30000001
Maximum16.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-02-19T18:37:25.074190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.30000001
5-th percentile1.4
Q12.5
median3.5999999
Q35
95-th percentile8.6000004
Maximum16.4
Range16.1
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.2342529
Coefficient of variation (CV)0.56196324
Kurtosis2.7914321
Mean3.9757991
Median Absolute Deviation (MAD)1.0999999
Skewness1.4466674
Sum8707
Variance4.9918861
MonotonicityNot monotonic
2025-02-19T18:37:25.293679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.5 154
 
7.0%
3.599999905 149
 
6.8%
2.799999952 148
 
6.7%
3.099999905 147
 
6.7%
2.200000048 136
 
6.2%
3.299999952 132
 
6.0%
3.900000095 120
 
5.4%
1.899999976 115
 
5.2%
4.199999809 100
 
4.5%
1.700000048 98
 
4.4%
Other values (42) 891
40.4%
ValueCountFrequency (%)
0.3000000119 4
 
0.2%
0.6000000238 12
 
0.5%
0.8000000119 23
 
1.0%
1.100000024 42
 
1.9%
1.399999976 75
3.4%
1.700000048 98
4.4%
1.899999976 115
5.2%
2.200000048 136
6.2%
2.5 154
7.0%
2.799999952 148
6.7%
ValueCountFrequency (%)
16.39999962 1
 
< 0.1%
15 1
 
< 0.1%
14.19999981 1
 
< 0.1%
13.60000038 2
 
0.1%
13.30000019 4
0.2%
13.10000038 1
 
< 0.1%
12.80000019 2
 
0.1%
12.5 1
 
< 0.1%
12.19999981 4
0.2%
11.89999962 6
0.3%

racha
Real number (ℝ)

High correlation 

Distinct76
Distinct (%)3.5%
Missing22
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean9.9587993
Minimum2.2
Maximum26.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.7 KiB
2025-02-19T18:37:25.385431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.2
5-th percentile4.6999998
Q16.6999998
median9.1999998
Q312.5
95-th percentile17.799999
Maximum26.4
Range24.199999
Interquartile range (IQR)5.8000002

Descriptive statistics

Standard deviation4.0362382
Coefficient of variation (CV)0.40529366
Kurtosis0.091910429
Mean9.9587993
Median Absolute Deviation (MAD)2.6999998
Skewness0.73700559
Sum21730.1
Variance16.291218
MonotonicityNot monotonic
2025-02-19T18:37:25.509231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.699999809 82
 
3.7%
9.199999809 74
 
3.4%
7.5 70
 
3.2%
8.899999619 70
 
3.2%
10 67
 
3.0%
6.099999905 66
 
3.0%
6.400000095 66
 
3.0%
8.600000381 65
 
2.9%
7.800000191 63
 
2.9%
7.199999809 63
 
2.9%
Other values (66) 1496
67.9%
ValueCountFrequency (%)
2.200000048 1
 
< 0.1%
2.799999952 1
 
< 0.1%
3.099999905 1
 
< 0.1%
3.299999952 5
 
0.2%
3.599999905 17
 
0.8%
3.900000095 22
 
1.0%
4.199999809 31
1.4%
4.400000095 20
 
0.9%
4.699999809 40
1.8%
5 56
2.5%
ValueCountFrequency (%)
26.39999962 1
 
< 0.1%
23.60000038 1
 
< 0.1%
23.29999924 2
0.1%
23.10000038 2
0.1%
22.5 3
0.1%
22.20000076 2
0.1%
21.89999962 2
0.1%
21.39999962 4
0.2%
21.10000038 2
0.1%
20.79999924 1
 
< 0.1%
Distinct146
Distinct (%)6.7%
Missing22
Missing (%)1.0%
Memory size17.3 KiB
2025-02-19T18:37:25.717202image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0563703
Min length5

Characters and Unicode

Total characters11033
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row01:10
2nd row23:50
3rd row11:10
4th row04:30
5th row06:50
ValueCountFrequency (%)
varias 123
 
5.6%
00:20 37
 
1.7%
00:10 33
 
1.5%
10:10 31
 
1.4%
10:00 28
 
1.3%
23:59 27
 
1.2%
12:50 27
 
1.2%
15:10 26
 
1.2%
11:00 26
 
1.2%
15:30 25
 
1.1%
Other values (136) 1799
82.4%
2025-02-19T18:37:25.960111image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3327
30.2%
: 2059
18.7%
1 1752
15.9%
2 873
 
7.9%
3 594
 
5.4%
5 505
 
4.6%
4 487
 
4.4%
a 246
 
2.2%
8 198
 
1.8%
9 195
 
1.8%
Other values (6) 797
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8236
74.6%
Other Punctuation 2059
 
18.7%
Lowercase Letter 615
 
5.6%
Uppercase Letter 123
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3327
40.4%
1 1752
21.3%
2 873
 
10.6%
3 594
 
7.2%
5 505
 
6.1%
4 487
 
5.9%
8 198
 
2.4%
9 195
 
2.4%
7 155
 
1.9%
6 150
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
a 246
40.0%
s 123
20.0%
r 123
20.0%
i 123
20.0%
Other Punctuation
ValueCountFrequency (%)
: 2059
100.0%
Uppercase Letter
ValueCountFrequency (%)
V 123
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10295
93.3%
Latin 738
 
6.7%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3327
32.3%
: 2059
20.0%
1 1752
17.0%
2 873
 
8.5%
3 594
 
5.8%
5 505
 
4.9%
4 487
 
4.7%
8 198
 
1.9%
9 195
 
1.9%
7 155
 
1.5%
Latin
ValueCountFrequency (%)
a 246
33.3%
s 123
16.7%
V 123
16.7%
r 123
16.7%
i 123
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11033
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3327
30.2%
: 2059
18.7%
1 1752
15.9%
2 873
 
7.9%
3 594
 
5.4%
5 505
 
4.6%
4 487
 
4.4%
a 246
 
2.2%
8 198
 
1.8%
9 195
 
1.8%
Other values (6) 797
 
7.2%

hrMedia
Real number (ℝ)

High correlation  Missing 

Distinct60
Distinct (%)3.1%
Missing251
Missing (%)11.4%
Infinite0
Infinite (%)0.0%
Mean74.056836
Minimum35
Maximum95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-02-19T18:37:26.047449image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35
5-th percentile55
Q170
median76
Q380
95-th percentile87
Maximum95
Range60
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.4150835
Coefficient of variation (CV)0.12713321
Kurtosis1.3584015
Mean74.056836
Median Absolute Deviation (MAD)5
Skewness-1.0056897
Sum144633
Variance88.643797
MonotonicityNot monotonic
2025-02-19T18:37:26.134689image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
77 131
 
5.9%
76 127
 
5.8%
79 113
 
5.1%
74 109
 
4.9%
75 108
 
4.9%
78 98
 
4.4%
80 93
 
4.2%
72 83
 
3.8%
73 76
 
3.4%
81 76
 
3.4%
Other values (50) 939
42.6%
(Missing) 251
 
11.4%
ValueCountFrequency (%)
35 1
 
< 0.1%
37 2
 
0.1%
38 1
 
< 0.1%
39 2
 
0.1%
40 3
0.1%
41 1
 
< 0.1%
42 2
 
0.1%
43 3
0.1%
44 3
0.1%
45 5
0.2%
ValueCountFrequency (%)
95 1
 
< 0.1%
94 3
 
0.1%
93 8
 
0.4%
92 5
 
0.2%
91 7
 
0.3%
90 13
0.6%
89 22
1.0%
88 18
0.8%
87 30
1.4%
86 32
1.5%

hrMax
Real number (ℝ)

High correlation  Missing 

Distinct44
Distinct (%)2.3%
Missing258
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean85.216855
Minimum47
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-02-19T18:37:26.222150image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile72
Q182
median86
Q390
95-th percentile94
Maximum99
Range52
Interquartile range (IQR)8

Descriptive statistics

Standard deviation6.6414019
Coefficient of variation (CV)0.077935309
Kurtosis2.3542461
Mean85.216855
Median Absolute Deviation (MAD)4
Skewness-1.1906215
Sum165832
Variance44.10822
MonotonicityNot monotonic
2025-02-19T18:37:26.308506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
89 145
 
6.6%
87 145
 
6.6%
88 143
 
6.5%
85 140
 
6.4%
86 138
 
6.3%
90 124
 
5.6%
91 121
 
5.5%
83 102
 
4.6%
84 99
 
4.5%
92 86
 
3.9%
Other values (34) 703
31.9%
(Missing) 258
 
11.7%
ValueCountFrequency (%)
47 1
 
< 0.1%
54 2
 
0.1%
55 2
 
0.1%
56 1
 
< 0.1%
57 2
 
0.1%
61 2
 
0.1%
62 3
0.1%
63 4
0.2%
64 6
0.3%
65 4
0.2%
ValueCountFrequency (%)
99 2
 
0.1%
98 2
 
0.1%
97 11
 
0.5%
96 20
 
0.9%
95 32
 
1.5%
94 49
 
2.2%
93 69
3.1%
92 86
3.9%
91 121
5.5%
90 124
5.6%

horaHrMax
Text

Missing 

Distinct144
Distinct (%)7.4%
Missing258
Missing (%)11.7%
Memory size17.3 KiB
2025-02-19T18:37:26.454379image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.3047276
Min length5

Characters and Unicode

Total characters10323
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.4%

Sample

1st row23:30
2nd row00:00
3rd row20:50
4th row23:59
5th row21:20
ValueCountFrequency (%)
varias 593
30.5%
00:00 91
 
4.7%
23:50 57
 
2.9%
23:59 49
 
2.5%
23:40 38
 
2.0%
23:30 24
 
1.2%
05:30 23
 
1.2%
00:10 21
 
1.1%
04:30 18
 
0.9%
00:20 17
 
0.9%
Other values (134) 1015
52.2%
2025-02-19T18:37:26.673231image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2530
24.5%
: 1353
13.1%
a 1186
11.5%
2 727
 
7.0%
i 593
 
5.7%
r 593
 
5.7%
V 593
 
5.7%
s 593
 
5.7%
1 578
 
5.6%
3 518
 
5.0%
Other values (6) 1059
10.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5412
52.4%
Lowercase Letter 2965
28.7%
Other Punctuation 1353
 
13.1%
Uppercase Letter 593
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2530
46.7%
2 727
 
13.4%
1 578
 
10.7%
3 518
 
9.6%
5 369
 
6.8%
4 334
 
6.2%
9 124
 
2.3%
6 84
 
1.6%
7 80
 
1.5%
8 68
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
a 1186
40.0%
i 593
20.0%
r 593
20.0%
s 593
20.0%
Other Punctuation
ValueCountFrequency (%)
: 1353
100.0%
Uppercase Letter
ValueCountFrequency (%)
V 593
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6765
65.5%
Latin 3558
34.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2530
37.4%
: 1353
20.0%
2 727
 
10.7%
1 578
 
8.5%
3 518
 
7.7%
5 369
 
5.5%
4 334
 
4.9%
9 124
 
1.8%
6 84
 
1.2%
7 80
 
1.2%
Latin
ValueCountFrequency (%)
a 1186
33.3%
i 593
16.7%
r 593
16.7%
V 593
16.7%
s 593
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10323
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2530
24.5%
: 1353
13.1%
a 1186
11.5%
2 727
 
7.0%
i 593
 
5.7%
r 593
 
5.7%
V 593
 
5.7%
s 593
 
5.7%
1 578
 
5.6%
3 518
 
5.0%
Other values (6) 1059
10.3%

hrMin
Real number (ℝ)

High correlation  Missing 

Distinct74
Distinct (%)3.8%
Missing258
Missing (%)11.7%
Infinite0
Infinite (%)0.0%
Mean59.657246
Minimum17
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.3 KiB
2025-02-19T18:37:26.759430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile34
Q151
median62
Q370
95-th percentile78
Maximum92
Range75
Interquartile range (IQR)19

Descriptive statistics

Standard deviation13.80787
Coefficient of variation (CV)0.23145336
Kurtosis-0.24147079
Mean59.657246
Median Absolute Deviation (MAD)9
Skewness-0.62191027
Sum116093
Variance190.65727
MonotonicityNot monotonic
2025-02-19T18:37:26.857132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
69 84
 
3.8%
70 76
 
3.4%
72 76
 
3.4%
68 75
 
3.4%
71 68
 
3.1%
73 67
 
3.0%
63 63
 
2.9%
62 62
 
2.8%
60 61
 
2.8%
66 55
 
2.5%
Other values (64) 1259
57.1%
(Missing) 258
 
11.7%
ValueCountFrequency (%)
17 2
 
0.1%
20 1
 
< 0.1%
21 3
 
0.1%
22 3
 
0.1%
23 8
0.4%
24 4
0.2%
25 2
 
0.1%
26 4
0.2%
27 9
0.4%
28 7
0.3%
ValueCountFrequency (%)
92 1
 
< 0.1%
91 2
 
0.1%
90 1
 
< 0.1%
89 1
 
< 0.1%
88 2
 
0.1%
87 2
 
0.1%
86 6
0.3%
85 6
0.3%
84 4
0.2%
83 6
0.3%

horaHrMin
Text

Missing 

Distinct146
Distinct (%)7.5%
Missing258
Missing (%)11.7%
Memory size17.3 KiB
2025-02-19T18:37:27.040429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.0745118
Min length5

Characters and Unicode

Total characters9875
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)0.1%

Sample

1st row09:20
2nd row19:50
3rd row00:50
4th row05:10
5th row09:30
ValueCountFrequency (%)
varias 145
 
7.5%
00:00 61
 
3.1%
08:50 30
 
1.5%
09:50 30
 
1.5%
15:50 28
 
1.4%
16:50 26
 
1.3%
10:30 25
 
1.3%
10:10 24
 
1.2%
15:10 24
 
1.2%
16:30 24
 
1.2%
Other values (136) 1529
78.6%
2025-02-19T18:37:27.294841image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3024
30.6%
: 1801
18.2%
1 1398
14.2%
2 651
 
6.6%
3 502
 
5.1%
5 472
 
4.8%
4 389
 
3.9%
a 290
 
2.9%
9 207
 
2.1%
7 201
 
2.0%
Other values (6) 940
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7204
73.0%
Other Punctuation 1801
 
18.2%
Lowercase Letter 725
 
7.3%
Uppercase Letter 145
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3024
42.0%
1 1398
19.4%
2 651
 
9.0%
3 502
 
7.0%
5 472
 
6.6%
4 389
 
5.4%
9 207
 
2.9%
7 201
 
2.8%
8 191
 
2.7%
6 169
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
a 290
40.0%
s 145
20.0%
r 145
20.0%
i 145
20.0%
Other Punctuation
ValueCountFrequency (%)
: 1801
100.0%
Uppercase Letter
ValueCountFrequency (%)
V 145
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9005
91.2%
Latin 870
 
8.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3024
33.6%
: 1801
20.0%
1 1398
15.5%
2 651
 
7.2%
3 502
 
5.6%
5 472
 
5.2%
4 389
 
4.3%
9 207
 
2.3%
7 201
 
2.2%
8 191
 
2.1%
Latin
ValueCountFrequency (%)
a 290
33.3%
s 145
16.7%
V 145
16.7%
r 145
16.7%
i 145
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9875
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3024
30.6%
: 1801
18.2%
1 1398
14.2%
2 651
 
6.6%
3 502
 
5.1%
5 472
 
4.8%
4 389
 
3.9%
a 290
 
2.9%
9 207
 
2.1%
7 201
 
2.0%
Other values (6) 940
 
9.5%

Interactions

2025-02-19T18:37:21.260598image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:14.496634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:15.333620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.225702image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.894434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.580071image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.265044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.071952image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.756410image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.450562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:21.328792image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:14.574454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:15.402766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.289020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.961362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.644207image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.342242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.144052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.827479image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.517063image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:21.401399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:14.644824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:15.495434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.358591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.031437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.711067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.417936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.220318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.897155image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.586516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:21.473258image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:14.711884image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:15.573032image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.422824image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.094762image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.778697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.496292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.285989image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.965072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.653692image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:21.546062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:14.782852image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:15.646024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.489770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.159942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.845326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.569165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.350433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.031203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.723703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:21.621467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:14.853397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:15.724171image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.556666image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.224937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.915557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.645291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.415407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.100891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.788901image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:21.700078image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:14.931999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:15.949400image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.627372image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.297561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.988118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.723191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.490387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.175484image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.865020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:21.771055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:15.026068image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.015117image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.690370image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.368414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.056500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.800089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.555722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.244277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:21.053159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:21.846482image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:15.136178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.084723image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.756257image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.437610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.125169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.877360image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.621615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.312061image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:21.121768image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:21.924984image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:15.241848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.154732image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:16.826105image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:17.507136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.195236image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:18.961991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:19.686084image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:20.379343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-02-19T18:37:21.188570image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-02-19T18:37:27.371192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
dirhrMaxhrMediahrMinprecrachatmaxtmedtminvelmedia
dir1.0000.1010.017-0.073-0.039-0.015-0.025-0.057-0.089-0.060
hrMax0.1011.0000.7570.5560.288-0.0110.1080.1410.160-0.024
hrMedia0.0170.7571.0000.8200.219-0.1330.0860.1530.206-0.040
hrMin-0.0730.5560.8201.0000.175-0.1340.0870.1860.2720.024
prec-0.0390.2880.2190.1751.0000.278-0.278-0.235-0.1850.170
racha-0.015-0.011-0.133-0.1340.2781.000-0.273-0.227-0.1770.744
tmax-0.0250.1080.0860.087-0.278-0.2731.0000.9770.914-0.196
tmed-0.0570.1410.1530.186-0.235-0.2270.9771.0000.978-0.114
tmin-0.0890.1600.2060.272-0.185-0.1770.9140.9781.000-0.031
velmedia-0.060-0.024-0.0400.0240.1700.744-0.196-0.114-0.0311.000

Missing values

2025-02-19T18:37:22.058250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-02-19T18:37:22.176267image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-02-19T18:37:22.389030image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

fechaindicativonombreprovinciaaltitudtmedprectminhoratmintmaxhoratmaxdirvelmediarachahorarachahrMediahrMaxhoraHrMaxhrMinhoraHrMin
02019-01-016083XMARBELLAMALAGA2.015.0000000.010.507:2919.50000011:102.01.95.301:1061.083.023:3038.009:20
12019-01-026083XMARBELLAMALAGA2.015.2000000.011.605:4118.79999915:1810.02.89.223:5054.081.000:0024.019:50
22019-01-036083XMARBELLAMALAGA2.014.6000000.012.806:0616.29999900:026.03.910.811:1073.078.020:5036.000:50
32019-01-046083XMARBELLAMALAGA2.013.8000000.011.8Varias15.70000012:288.05.310.604:3067.073.023:5960.005:10
42019-01-056083XMARBELLAMALAGA2.015.0000000.010.406:2319.60000009:332.02.55.006:5071.082.021:2038.009:30
52019-01-066083XMARBELLAMALAGA2.013.8000000.010.307:3817.29999915:0924.01.75.815:2071.080.0Varias45.015:10
62019-01-076083XMARBELLAMALAGA2.013.4000000.09.303:2517.40000009:454.01.95.021:1069.081.002:3053.003:30
72019-01-086083XMARBELLAMALAGA2.013.6000000.011.222:5916.00000016:178.03.16.911:5072.082.005:1062.012:50
82019-01-096083XMARBELLAMALAGA2.014.2000000.010.707:4017.70000115:3626.02.56.421:3072.078.000:0029.023:50
92019-01-106083XMARBELLAMALAGA2.016.2000010.013.419:4019.00000010:3731.03.911.706:1056.077.022:2028.010:40
fechaindicativonombreprovinciaaltitudtmedprectminhoratmintmaxhoratmaxdirvelmediarachahorarachahrMediahrMaxhoraHrMaxhrMinhoraHrMin
21942024-12-236083XMARBELLAMALAGA2.016.2000010.013.004:1819.50000010:086.04.715.012:5074.082.007:4042.010:00
21952024-12-246083XMARBELLAMALAGA2.015.7000000.013.422:0118.00000009:277.03.98.913:3072.082.0Varias63.012:50
21962024-12-256083XMARBELLAMALAGA2.015.1000000.011.602:4718.60000015:434.04.410.018:2062.074.0Varias39.015:50
21972024-12-266083XMARBELLAMALAGA2.016.7999990.015.303:1518.29999913:2099.05.310.6Varias80.084.017:4065.002:40
21982024-12-276083XMARBELLAMALAGA2.016.6000000.015.923:1617.20000100:087.08.613.308:4075.081.000:3069.022:40
21992024-12-286083XMARBELLAMALAGA2.015.6000000.014.408:4416.90000016:108.07.213.102:3071.078.022:3065.010:30
22002024-12-296083XMARBELLAMALAGA2.015.9000000.014.806:1717.00000020:248.06.111.722:4072.076.0Varias68.009:30
22012024-12-306083XMARBELLAMALAGA2.015.8000007.014.407:3917.29999915:308.04.213.101:1070.078.0Varias58.009:40
22022024-12-316083XMARBELLAMALAGA2.014.6000000.012.822:1616.400000Varias10.03.310.003:1072.087.002:2064.012:50
22032025-01-016083XMARBELLAMALAGA2.014.2000000.012.205:1516.10000015:448.03.37.513:1072.076.0Varias67.012:50

Duplicate rows

Most frequently occurring

fechaindicativonombreprovinciaaltitudtmedprectminhoratmintmaxhoratmaxdirvelmediarachahorarachahrMediahrMaxhoraHrMaxhrMinhoraHrMin# duplicates
02019-07-016083XMARBELLAMALAGA2.023.0000000.020.00000005:1626.10000017:4426.04.49.214:2075.088.004:4063.017:502
12020-01-016083XMARBELLAMALAGA2.014.6000000.011.70000023:1617.60000011:382.01.44.401:2071.086.023:5059.011:302
22020-07-016083XMARBELLAMALAGA2.0NaN0.0NaNNaNNaNNaN27.03.67.513:20NaNNaNNaNNaNNaN2
32021-01-016083XMARBELLAMALAGA2.013.1000000.010.200000Varias16.00000016:1123.04.413.600:1046.083.000:0023.016:302
42021-07-016083XMARBELLAMALAGA2.025.2999990.021.79999904:1128.79999915:0028.02.54.720:0083.090.019:3071.015:102
52022-01-016083XMARBELLAMALAGA2.015.1000000.012.70000000:1117.50000015:176.02.86.411:3085.094.0Varias78.0Varias2
62022-07-016083XMARBELLAMALAGA2.024.4000000.021.10000000:1327.79999917:557.06.415.010:3076.088.004:5069.0Varias2
72023-01-016083XMARBELLAMALAGA2.014.8000000.011.50000004:1818.20000114:348.02.57.213:1058.071.023:4046.009:402
82023-07-016083XMARBELLAMALAGA2.026.1000000.023.20000101:1929.00000016:0810.02.55.808:4079.086.0Varias75.000:002
92024-01-016083XMARBELLAMALAGA2.016.799999NaN14.20000006:3019.29999915:3434.01.79.408:2068.078.017:3051.000:202